Scanning ir sensor for gas safety and emissions monitoring

ABSTRACT

Apparatus and methods for rapidly detecting, localizing, imaging, and quantifying leaks of natural gas and other hydrocarbon and greenhouse gases. Scanning sensors, scan patterns, and data processing algorithms enable monitoring a site to rapidly detect, localize, image, and quantify amounts and rates of hydrocarbon leaks. Multispectral short-wave infrared detectors sense non-thermal infrared radiation from natural solar or artificial illumination sources by differential absorption spectroscopy. A multispectral sensor is scanned to envelop an area of interest, detect the presence and location of a leak, and raster scan the area around the leak to create an image of the leak. The resulting absorption image related to differential spectral optical depth is color mapped to render the degree of gas absorption across the scene. Analysis of this optical depth image, with factors including known inline pressures and/or surface wind speed measurements, enable estimation of the leak rate, i.e., emission mass flux of gas.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority of U.S. Provisional Patent ApplicationNo. 62/472,463, filed Mar. 16, 2017 and U.S. Provisional PatentApplication 62/587,304. filed Nov. 16, 2017.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

(Not Applicable)

SEQUENCE LISTING

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BACKGROUND OF THE INVENTION

This invention consists of sensors and algorithms to scan a sitecontaining natural gas and related infrastructure, and automaticallydetect, localize, image and quantify hydrocarbon gas leaks using ashort-wave infrared radiation detector in combination with multiplespectral filters under natural solar or artificial illumination.Particular embodiments recited address detection and quantification ofmethane gas leaks. Quantification includes total volume, total mass, andemission/leak rates of methane and other gases of interest. Theinvention is suitable for both gas safety (rapid detection) andemissions monitoring applications. Several embodiments described supportapplications to installed fixed site monitoring, relocatable work sitemonitoring, and hand portable site inspection. These and similarembodiments are applicable more generally to hydrocarbon gases, liquids,emulsions, solids, and particulates, toxic gases, and key greenhousegases.

Natural gas leaks create both safety and environmental hazards, andoccur along the entire gas supply chain from the well to the street(so-called upstream, midstream, and downstream sectors). Methane, theprimary constituent of natural gas is combustible in air, and is also apotent greenhouse gas. Other hydrocarbons found in natural gas, as wellvapors emanating from liquids separated from gas and oil include ethane,propane, butane, pentane, hexane, octane, and heavier hydrocarbons,which form volatile organic compounds that generate smog which is ahealth hazard. Thus, there are compelling reasons to detect leaks ofmethane gas and other hydrocarbon gases, so that such leaks can berepaired. However, in order to repair such leaks, it is necessary toalso localize the leak, and in order to prioritize repairs it isdesirable to quantify the leak in terms of leak rate or emission flux.Estimating gas emission flux is also needed to assess environmentalimpact of greenhouse gases. Moreover, it is desirable to have a means tomonitor or inspect wide areas for such leaks and do so quickly from asafe and practical standoff distance, while maintaining the ability topinpoint the leak location and estimate the leak rate. It is alsodesirable to conduct effective leak monitoring in the presence ofnaturally occurring ambient gases and vapors, such as water vapor, andregardless of the relative temperature between leaked gas and thebackground environment. A cost-effective solution is also necessary ifsuch solutions are to be broadly adopted and utilized.

Gas detectors can be classified according to their coverage extent, aseither spot sensors, line sensors or area sensors. Spot sensors, oftenreferred to as sniffers, draw in a local sample of air and detect thepresence of a combustible or toxic gas by means of various analyticalmethods. They can be fixed in place for continuous monitoring, or handportable for inspections, but they require direct sampling in place andprovide very limited coverage. They may provide concentrationmeasurements, but do not provide leak rate estimates. Otherinstrumentation is available to locally sample (as opposed to image)known leaks in order to provide an estimate of leak rate, but they tooprovide only local coverage and require direct collection of gas fromthe leaking component.

Optical line sensors, also known as open-path gas detectors, employoptical means to detect gas that lies along the line between a dedicatedlight emitter (e.g., laser, tunable laser, or narrowly focused broadbandsource) and a dedicated photo-detector (or multiple photo-detectors).Such detectors exploit the absorption of light (typically in differentparts of the infrared spectrum) at select wavelengths characteristic ofthe molecular composition of the gas of interest. These sensors detectgas present anywhere along the line between the light emitter and thephoto-detector (or between combined emitter/detector assembly and aremote reflector if the optical path is folded), but they cannotdetermine where along the path the gas is, nor from where it came, andhas limited coverage to only the narrow open path between emitter anddetector. By utilizing multiple wavelengths of light, such sensors canmeasure column density of gas along the open path, but cannot measure orestimate concentration nor leak rate. Open-path sensors can be installedin place, hand portable, or mobile aboard ground and air vehicles. Inorder to achieve area coverage from a standoff distance, it isrecognized that imaging sensors offer many advantages over spot and linesensors, in that they can detect the presence of gas and possiblylocalize the leak source.

Several gas imaging technologies have been proposed, developed,patented, and are commercially available. They are all based on theabsorption of infrared light at wavelengths characteristic of themolecules of interest. For methane and hydrocarbons in general, mostimagers operate in select bands of the mid-wave infrared and long-waveinfrared spectrum. The leading commercially available gas imagingsensors operate in only a single narrow band of the mid-wave infraredspectrum, and do not provide quantitative data, only pictures to beinterpreted by the human operator. Other imaging sensors utilizemultiple spectral bands in the long-wave infrared (the so-called“molecular fingerprint region”) to detect and discriminate amongdifferent hydrocarbon gases, and to quantify the column density of gasat each pixel of the image. Such systems have proven to be bothexpensive and have significant shortcomings. These mid-wave andlong-wave infrared sensors rely on thermally emitted light from thebackground to illuminate the gas that will absorb at select wavelengthsas detected by the imaging sensors. This requires that the backgroundand gas differ in temperature by at least several degrees Celsius,otherwise the light absorbed (or emitted) by the gas will not providesufficient signal contrast to be reliably detected by the humanoperators of these thermal sensors. For example, in the case of surfaceemissions of natural gas due to an underground pipe leak, or methaneemissions from a landfill, the gas percolates up through the soil andreaches thermal equilibrium with the soil by the time it emerges fromthe ground. Thus, there is little or no thermal contrast between the gasand the ground, and so cannot be reliably detected by a thermal infraredsensor. Another major shortcoming of mid-wave and long-wave gas imagingsensors is their poor performance in the presence of water vapor (highhumidity, steam), fog and light rain. This is because the spectrum ofwater overlaps with key spectral features of methane in both themid-wave and long-wave infrared spectral regions. Thus, water vapor willmask the presence of a methane leak, and conversely, water vapor willtrigger a false alarm for methane. As both water vapor and methane areless dense than air, they both rise due to buoyancy and look alike in aspectrally filtered mid-wave or long-wave infrared image. Additionally,all mid-wave infrared and some long-wave infrared gas imaging sensorsrequire cryogenic cooling, which is both expensive and unreliable. It ispreferable to utilize only thermo-electric cooling to reduce darkcurrent in gas imaging sensors. Finally, none of the available gasimaging sensors provides a capability to estimate leak rate from a hole,or emission flux from a surface. Some can provide column density of gasat each pixel, and using spatial information of the imaged gas jet,plume or cloud, one can then estimate local or average gasconcentration.

In order to overcome the above-cited shortcomings of thermal infraredbased imaging sensors for gas detection, it is possible to utilizedifferential absorption gas imaging in the short-wave infrared part ofthe spectrum. Atmospheric scientists using satellite-borne sensors likeLandsat and SCIAMACHY have exploited this. It enables the detection ofmethane, other hydrocarbons, carbon dioxide, and other gases in theatmosphere based on molecular absorption of natural sunlight, withoutconfusion of intervening water vapor. Such space-based imagingtechnologies provide synoptic scale maps of column densities ofgreenhouse gases and other air pollutants.

It is the purpose of this invention to provide sensors and methods thatenable rapid gas leak detection and localization, imaging, andquantification of leak rate or emission mass flux, utilizingmultispectral scan-based imaging in the short-wave infrared incombination with the hydrodynamics of turbulent gas jets and buoyantplumes. Multiple embodiments of the invention are described and havebeen developed, that are applicable more generally to natural gas andother hydrocarbon gases, liquids, emulsions, solids, and particulates,and to emissions monitoring of greenhouse gases such as methane andcarbon dioxide.

BRIEF SUMMARY OF THE INVENTION

This invention describes apparatus and methods for detecting,localizing, imaging, and quantifying leaks of natural gas and otherhydrocarbon and greenhouse gases, with application to both safety andemissions monitoring. It extends the apparatus and methods described inU.S. Provisional Patent Application 62/338,255, Hydrocarbon Leak Imagingand Quantification Sensor, filed 18 May 2016 by Waxman et al. ofMultiSensor Scientific, Inc.

This invention describes scanning sensors, scan patterns, and dataprocessing algorithms that enable monitoring a site of extended area, inorder to rapidly detect, localize, image, and quantify amounts and ratesof hydrocarbon leaks. A small number of multi spectral short-waveinfrared detectors are used to sense non-thermal infrared radiation fromnatural solar or artificial illumination sources. More specifically,several embodiments of sensor systems are described that incorporateshort-wave infrared detectors sensitive in the range of approximately1.0 through 2.6 microns, in combination with approximately five spectralfilters selected to create multiple spectral bands at least in the rangeof 1.9 to 2.5 microns, with respect to molecular spectral featuresassociated with methane, ethane, propane, butane, carbon dioxide, andammonia, while avoiding strong absorption features of water vapor.Detection is accomplished via absorption spectroscopy using naturalsunlight or artificial illumination in direct transmission through a gasto the sensor, or reflected off a background surface with gas locatedbetween the background and the sensor.

The multispectral sensor can be scanned across a scene or an extendedsite using various scanning patterns designed to rapidly detect leaks,then localize the leaks, image them and quantify them in both volume (ormass of gas) and leak rate. Leaks can be detected, imaged andquantified, from pressurized pipelines, valves, and vessels aboveground, as well as underground leaks as they emerge from the surface.The system can adapt to changing illumination conditions (brightness andspectrum) as well as changing background material reflectivity. Scanningcan be accomplished using mechanical means involving a computercontrolled precision pan-tilt unit, or using a combination of resonantvibrating mirrors, motor driven mirrors, and micro-machined mirrorarrays.

The multispectral SWIR imagery is processed in real-time to yield anabsorption image related to the differential spectral optical depth, orequivalently column density, of an intervening hydrocarbon gas such asmethane, the major constituent of natural gas. Other hydrocarbon andgreenhouse gases can be imaged simultaneously in the case of gasmixtures, as is typically the case. Recognition of individualconstituent gases is accomplished using established pattern learning andrecognition techniques commonly employed in multispectral andhyperspectral image processing.

The resulting absorption imagery is color mapped to render the degree ofgas absorption across the scene, and overlaid on an optically registeredcolor visible image that provides context. In the case of gas leakingfrom a hole or crack in a pressurized pipe, flange, valve or vessel, theescaping gas forms a turbulent jet or plume that is visible in theabsorption image and from which the leak can be localized. The inventedmethods estimate both the diameter of the effective hole and the massflux of leaking methane (or other gas) from the data present in thisabsorption image, if the internal pressure driving the leak is knownapproximately.

In the case of underground gas leaks, such as due to municipal gasinfrastructure, the gas percolates through the subsurface soil andemerges at the surface, often in disconnected surface patches. Thesesurface emissions diffuse into a thin layer next to the ground and rise(in the case of natural gas) due to buoyancy, but are quickly blown byground-level winds. The invented methods estimate both the mass of gasand the mass flux from a surface patch by combining the absorptionimagery with wind speed and direction measured near ground level.Estimation formulas are derived for the case of steady winds and gustingwinds. The invention also addresses mass flux estimation from wide-areasurface emissions, such as the case with large landfills or open pitmines and tailing ponds such as found in the Canadian oil sands region.When emissions occur over extended surfaces, a stratified methaneatmosphere is established over the surface, with a buoyant verticalmethane flux balanced by the surface emission flux. By sensing theabsorption imagery from a known height/altitude above the surface, anestimate of surface methane emissions is established.

A real-time functional prototype of a leak imaging and quantificationsensor has been built, and a graphical user interface that controls thesensor has been implemented on a touch-screen tablet display. Exampleimagery and data is shown in the figures. A similar scan imagingprototype is currently under development.

This invention has several key advantages over thermal infrared gasimaging sensors that operate in the mid-wave or long-wave infrared partsof the spectrum. This includes the ability to detect and quantify leakedgas with no temperature difference relative to the background, as theinvention utilizes short-wave infrared light provided by naturalsunlight or by lamps of appropriate color temperature, and does not relyon a thermal contrast between gas and the background or a background ofvarying temperature. The detectors suitable for use in this invention donot require cryogenic cooling, using instead thermo-electric coolingthat is more reliable and less expensive than cryogenic coolers such asa Stirling engine or liquid nitrogen. The invention can also detect gasleaks in the presence of humid air, steam and fog, as the hydrocarbonfeatures detected in the SWIR do not overlap spectral regions wherewater vapor absorption is significant, which is a major shortcoming forgas imagers operating in other parts of the infrared spectrum. Theembodiment of a scan imager provides a cost-effective design, byallowing the use of a small number of discrete photo-diodes or smallphotodiode array. This approach trades away video-rate imaging forcost-effective but slower image scanning, which is satisfactory fornumerous applications. Finally, the use of a rapid scanning deviceenables site-wide monitoring for gas leaks with a response time quickenough for safety applications (approximately 10 second response time).Many flexible scan patterns can be implemented and rapidly switchedbetween in an automated fashion. This invention documents severalexamples of scan patterns to detect, localize and image leaks. Theseexamples are meant to be illustrative but not exhaustive. Yet theconcept and advantages should be clear. This enables the invention to beuseful for gas safety, leak detection and repair, and gas emissionsmonitoring applications.

This invention and its various embodiments will be useful in detecting,localizing, imaging, and quantifying natural gas leaks from componentsalong the entire gas supply chain, from the well head to compressors totransmission pipelines to gate stations and underground distributionnetworks. This invention has also been shown to be useful in detectingliquid oil spills on land, sand, seawater, and sea ice. Otherembodiments of the invention will prove useful in detecting oilemulsions at sea and tar balls on beach. The embodiments of theinvention described herein are suitable for packaging in the form ofinstalled and relocatable fixed-site monitoring sensors, relocatablework-site safety sensors, and hand portable leak inspection sensors, allof which utilize small numbers of SWIR detectors and spectral filters ina scanning configuration.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

Aspects of the described embodiments are more evident in the followingdescription, when read in conjunction with the attached Figures.

FIG. 1A illustrates the absorption spectra in the 1.9-2.6 micron rangeof the short-wave infrared for the gases methane, ethane, propane,butane, carbon dioxide, ammonia, and water vapor;

FIG. 1B illustrates normalized 5-band spectra for the same gases as inFIG. 1A, where the ideal spectral bands have bandwidths of 100nanometers, and band centers at wavelengths of 2000, 2100, 2250, 2350,and 2450 nanometers;

FIG. 2A illustrates a 5-element array of discrete photo-detectors with a5-band spectral filter mosaic positioned over the photo-detector array;

FIG. 2B illustrates the use of a small 9×9 element array ofphoto-detectors where sub-arrays of 3×3 detectors form macro-pixels eachcovered by a different spectral filter, where the filters are arrangedin a 3×3 spectral filter mosaic;

FIG. 3 is a system block diagram of the scanning sensor system for leakdetection, localization, imaging, and quantification;

FIG. 4 illustrates the invention monitoring a gas wellpad, with thesensor and illuminator mounted on a pan-tilt unit atop a mast;

FIG. 5A diagrams a plan view of a square site of dimensions L×L (exampleL=10 meters) with the sensor S located at one corner of the site;

FIG. 5B diagrams a side view of the L×L square site, with sensor Smounted atop a mast of height ¾L located at one corner of the site;

FIG. 6 illustrates a plan view of a square site covered by a polarcoordinate raster scan pattern relative to the sensor S;

FIG. 7 illustrates a plan view of a square site with a boundary scanpattern relative to the sensor S;

FIG. 8 illustrates a plan view of a square site with a localization scanpattern overlaid on the boundary scan pattern of FIG. 7;

FIG. 9 illustrates a plan view of a square site with a local polarraster scan across a shifted sector within which the leak has beenlocalized;

FIG. 10A shows a real-time absorption image of a methane gas jet exitinga 1 mm orifice from a test manifold pressurized to 1300 psig;

FIG. 10B shows three profiles of differential optical depth across themethane gas jet of FIG. 10A, corresponding to pixel values sampled alongthe lines labeled a, b, and c;

FIG. 11A shows a graph of the estimated jet width along the axis of themethane jet of FIG. 10A, and a least-squares linear regression to thesedata points;

FIG. 11B shows a graph of the integrated differential optical depthacross the width of the jet, along the axis of the methane jet of FIG.10A, and a least-squares linear regression to these data points;

FIG. 11C shows a graph of the ratio of integrated differential opticaldepth to estimated jet width (i.e., the average differential opticaldepth) along the axis of the methane jet of FIG. 10A, and aleast-squares linear regression to these data points;

FIG. 12 shows a graph of the integrated differential optical depthacross the width of a methane jet exiting a narrow slit (i.e., anidealized “crack”) at a pressure of 60 psig;

FIG. 13A illustrates for a set of experiments, a graph of the interceptvalue of average differential optical depth relative to the diameter ofthe leak hole vs. the internal pressure (in Bar) driving a methane jetfrom orifices of 1 mm and 0.75 mm, and compares the data to a smoothpower-law curve;

FIG. 13B illustrates for an extensive set of experiments including roundand slit orifices of various sizes, a graph of the measured mass flux ofmethane (in grams per minute per unit area of the hole) vs. the internalpressure (in psig) driving the methane jet, and a least-squares linearfit to the data;

FIG. 14A shows an example gas absorption image for a field test of 100%methane exiting a 0.38 mm round orifice at an exit pressure of 938 psigin wind;

FIG. 14B compares image-based estimates of methane mass outflow toinstrumented measurements of methane mass inflow, for a set ofexperiments conducted with round-hole orifices at various exit pressuresup to 1000 psig, in winds measured between 0-10 miles/hour;

FIG. 15A shows an example gas absorption image of a residential streetin the Boston area;

FIG. 15B shows an example absorption image of natural gas leaking from asmall pipe at 4 feet below the surface of a field;

FIG. 16A illustrates a plan view of a surface patch emitting methane (ornatural gas) at rate Q_(m) grams/sec on average within its irregularboundary with ground-level winds of speed V; and

FIG. 16B illustrates a side view of the sensor S mounted atop a mast onthe ground within a wide-area surface emission site.

DETAILED DESCRIPTION OF THE INVENTION

The mathematical methods that underlie this invention are described andbuild upon those described in U.S. Prov. Pat. Appl. No. 62/338,255. Thedescription that follows may refer to methane as the gas of interest,though much of the formulation applies to other pure gases and gasmixtures except where positive buoyancy is assumed (and noted). Theformulation may refer to the use of five spectral bands, however, thisis only by way of example and not meant to be restrictive; this is ageneral multispectral formulation in the short-wave infrared. Indeed,many of the sensor designs and scanning concepts apply equally to otherparts of the infrared spectrum, including mid-wave and long-waveinfrared regions sometimes used for detecting gas leaks by absorption(or emission) of thermal radiation.

Principals of Gas Absorption Imaging

This invention detects gas leaks via differential absorption imagingspectroscopy in the range 1.9 to 2.6 microns, exploiting spectralfeatures of hydrocarbons in the short-wave infrared (SWIR) region,primarily in the wavelength range of 2.0 to 2.5 microns. Thesewavelengths are not typically associated with those in the thermalemission regions of the mid-wave infrared (MWIR) and long-wave infrared(LWIR) for objects at terrestrial temperatures. Appreciable thermalemission at around 2.0 microns requires objects at temperatures ofaround 1200° C. Instead, this invention relies on illumination sourceslike natural sunlight and lamps of color temperature near 1200° C. Thus,the invention can detect hydrocarbons at the same temperatures as theirbackgrounds by using external illumination instead of thermally emittedlight.

SWIR radiation from the sun or broadband artificial illumination,directly or in reflection off background objects, transmits through theambient atmosphere, passes through a gas jet or plume emanating from asource such as for example a leak, continues towards the sensor where itis filtered into multiple spectral bands and detected on aphoto-detector array that is sensitive to SWIR photons. Both theatmosphere and the gas absorb some of the light at wavelengthscharacteristic of the materials that comprise these media. In the caseof natural gas the primary absorber is methane, while for the atmospherethe primary absorbers are water vapor and other ambient gases that mayinclude methane as well as carbon dioxide. Incident light is alsoscattered out of the transmission path by particulates in the atmosphereand the gas leak itself. Light that is absorbed by the gas issubsequently reemitted in all directions, resulting in a reduction oflight at characteristic wavelengths that is transmitted in the directionfrom the light source towards the sensor.

When imaging methane and other hydrocarbons, it is common to exploittheir strong spectral features in the MWIR and LWIR, as the absorptionin those spectral regions is greater than in the SWIR. However, it isimportant to consider the effects of water vapor absorption by theintervening atmosphere. In most applications, the physical extent of agas jet, plume or cloud is small compared to the length of atmospherethat the light will propagate through on its way to the sensor. Thus,appreciable absorption may occur at wavelengths characteristic of watervapor, depending on the humidity of the air or the presence of fog orsteam in optical field-of-view. It is therefore important to considerthe relative absorption of methane to water vapor at the wavelengthsthat characterize methane. Despite the relatively weaker absorptioncross-section for methane in the SWIR compared to the MWIR and LWIR, ithas significantly higher absorption ratio to water vapor in the SWIR.Thus, for imaging gas in the presence of humidity or fog or steam, theSWIR region has particular advantage over both the MWIR and LWIRspectral regions. For many applications, this is an advantage, despitethe lower absorption cross-section in the SWIR.

FIG. 1A shows a plot of absorption spectra from 1.8 to 2.6 microns rangeof the SWIR for the gases methane, ethane, propane, butane, carbondioxide, ammonia, as well as for water vapor. From FIG. 2A it can beseen that the hydrocarbons possess broad feature complexes from 2.2 to2.5 microns with much overlap in the range of 2.2 to 2.4 microns.Methane can be separated from the other hydrocarbons by its reducedabsorption in the 2.4 to 2.5 micron range. It is also apparent thatthese gases have spectral features in the SWIR that lie between thestrong water vapor features below 2.0 microns and above 2.5 microns. Asis well known in the art, similar absorption features are present in theSWIR for liquid crude oil, oil-water emulsions, asphalt and tar.

Normalized 5-band spectra for the same gases are depicted in FIG. 1B.Here, the ideal spectral bands have bandwidths of 100 nanometers, andband centers at wavelengths of 2000, 2100, 2250, 2350, and 2450nanometers.

The invention described here has been reduced to practice by buildingfunctional prototypes of a multispectral video imager and a scan imagerfor methane imaging, detection and quantification. The prototypedual-band video sensor images at 20 frames per second and displays gasabsorption imagery overlaid on color visible imagery of the scene on atouch-screen user display. The prototype system is hand-portable andinterfaces to external networks via both wireless and wired interfaces.The prototype 6-band scan sensor creates imagery of gas over aprogrammable and variable field-of-regard, by combining raster scanningwith super-resolution image processing. The flexibility of switchingamong a variety of scan patterns enables this sensor to support both gassafety applications and emissions monitoring applications, in acost-effective manner. This scan imager is suitable for mast-mounting tooverlook wide-area installations, using a programmable pan-tilt unit toeffect scanning. An alternative embodiment replaces the pan-tilt unitwith scanning mirrors or a combination of scanning mirror and rotatingoptics, to enable compact packaging for a hand-portable gas imaging andquantification camera.

Imaging Sensor Embodiments

There are several different semiconductor materials that can be used tofabricate the basic photo-detector sensitive to the SWIR spectrum oflight from approximately 1.8 to 2.6 microns, with a dark-current thatcan be suitably reduced by thermo-electric cooling. These includeso-called extended-response indium gallium arsenide (extended-InGaAs)commonly grown on an indium phosphide (InP) lattice-mismatchedsubstrate, and the recently developed type-II quantum wells made fromalternating layers of InGaAs and gallium arsenide antiminide (GaAsSb)grown on an InP lattice-matched substrate. These two materials havedifferent spectral response characteristics, but both can be used fordetecting the hydrocarbons that comprise natural gas, and in particular,methane as well as VOCs. They also have different manufacturing yieldsdue to their lattice structures. Thus, extended-InGaAs photo-detectorsare only available as discrete photo-detectors and one-dimensionalarrays but not as two-dimensional arrays, while type-II InGaAs/GaAsSbphoto-detectors have been successfully fabricated and demonstrated astwo-dimensional arrays. Mercury cadmium telluride (MCT) is a commoninfrared detector material that can also be used for imaging in theextended SWIR; however, its high dark-current requires cryogenic coolingwith, for example, a Stirling engine to achieve useful signal-to-noiseratios.

All of the multi-spectral SWIR detector configurations described andshown herein may utilize scanning and focusing optics in order to createtwo-dimensional spectral imagery from which a gas detection imager canbe created. As is known to one of ordinary skill in the art, all thedisclosed detector embodiments lend themselves to packaging in hand-heldsystems, and can also be configured to operate on moving platforms suchas ground vehicles, airborne rotorcraft and fixed-wing platforms, ships,rotating mast-mounted systems, translating rail-mounted systems, andorbiting satellites.

FIG. 2A shows a 5-element array of discrete photo-detectors with a5-band spectral filter mosaic positioned over the photo-detector array.Five discrete photo-detectors, PD1, PD2, PD3, PD4, and PD5, are arrangedin a three row stack. Each photo-detector has a respective analogreadout circuit and either dedicated or optionally sharedanalog-to-digital converter. Each photo-detector is covered with aseparate spectral filter island, F-A, F-B, F-C, F-D, and F-E,respectively. In practice, the five discrete photo-detectors are to bemounted on a common thermo-electric cooler and enclosed in ahermetically sealed package with a transparent window. The spectralfilters can be located outside the window aligned with thephoto-detectors below, or be located on the inside of the window, orserve as the window itself. With the appropriate lens, thisconfiguration forms the equivalent of a single multi-spectral SWIRpixel, or alternatively a small multispectral detector array. Thisconfiguration can clearly be extended to more or fewer discretephoto-detectors, each with its own spectral filter.

FIG. 2B illustrates the use of a 9×9 element monolithic array of smallphoto-diode pixels where sub-arrays of 3×3 pixels form macro-pixels,each macro-pixel covered by a different spectral filter, and where thefilters are arranged in a 3×3 spectral filter mosaic. While 3×3 pixelsub-arrays are illustrated, each filter island of the mosaic overlays atwo-dimensional rectangular sub-array of small pixels. Upon readout ofthe entire detector array, each sub-array of pixels corresponding to thesame filter island can be combined into a macro-pixel. Thisconfiguration trades off reduced spatial resolution for increased signalin a two-dimensional array of very small photo-detectors.Two-dimensional 2.5 um-SWIR type-II InGaAs/GaAsSb imaging arrays ofvarious numbers of pixels, for example 64×64 pixels, can be adapted foruse in the illustrated embodiment.

Gas Imaging Sensor System

FIG. 3 is a system block diagram of a scanning sensor system for leakdetection, localization, imaging, and quantification. The elementsdepicted within FIG. 3 are:

-   -   SWIR SWIR photo-detector array with read-out electronics;    -   SFM Spectral Filter Matrix located over the SWIR detector array;    -   L Lens for the SWIR photo-detector array, located in front of        the SFM;    -   RGB Color Visible micro-camera with lens;    -   LRF Laser Range Finder (near IR);    -   LP Laser Pointer (visible “red dot”);    -   PTU Pan-Tilt Unit for scanning sensors across a site in two        dimensions;    -   Lum SWIR broadband illuminator to augment solar illumination;    -   C1 micro-Controller with A/D converter for sampling SWIR        signals;    -   C2 micro-Controller for controlling PTU motion and illuminator        brightness;    -   P1 micro-Processor #1 (real-time SWIR signal processor);    -   P2 micro-Processor #2 (all other sensors & GUI        requests/display);    -   GPS Global Positioning System receiver;    -   IMU Inertial Measurement Unit (6 degrees of freedom);    -   Mag Magnetometer compass;    -   Wx Weather sensors (T, P, RH, wind speed & direction);    -   GUI Graphical User Interface on touchscreen tablet;    -   E/C Ethernet/Cloud; and    -   PC Personal computer remotely running the system via the cloud.

The discrete photo-detectors and spectral filter mosaic (SFM) of FIG. 2Aor 2B form a single multispectral pixel by means of a defocusing lens(L), and this sensor is scanned across the scene in two directions bymounting it atop a high-accuracy pan-tilt unit (PTU) controlled bymicro-Controller (C2). Alternatively, the discrete photo-detectors andspectral filter mosaic can be treated as a multispectral detector array,with lens (L) focusing the scene onto the array. The array is thenscanned over the scene, and each spectral detector forms a spectralimage, which must then be geometrically warped in order to register allspectral images to a common reference frame (e.g., at the center of thearray). Alternative scanning mechanisms may be used to replace thehigh-accuracy pan-tilt unit, which may include mechanically positionedmirrors (e.g., galvanometer driven mirrors, resonant scanning mirrors,electrically-actuated micro-mirrors, rotary stage positioned mirrors,combinations of these mechanisms, and combinations of these mechanismswith a pan-tilt unit). Two-dimensional imagery is created by rasterscanning across a desired, and possibly variable, field-of-regard. Inorder to create imagery of higher resolution than that obtained directlyby scanning this sensor with its own narrow field-of-view, it is usefulto employ spatial over-sampling in combination with super-resolutionimage processing, which is widely discussed in the literature.Two-dimensional scanning can also be accomplished in a compactconfiguration, for example, by a pair of scanning mirrors or a pair ofrotating prisms. Two-dimensional imaging can also be achieved using asingle scanning mirror combined with physical movement of the imagingsensor (e.g., translation or rotation in a direction perpendicular tothe scan mirror motion) such as by mounting the sensor upon a movingplatform (e.g., truck-mounted, airborne, rail-mounted, orbiting) orrotating the sensor in a mast-mounted configuration.

The imaging sensor system of FIG. 3 may also include one or more visiblecolor (RGB) or black and white cameras, laser range finder (LRF) tomeasure distance from the sensor to a detected leak, global positioningsystem (GPS) sensor to determine sensor location (and indirectly leaklocation), inertial measurement unit (IMU) to sense linear androtational accelerations including direction of gravity, magnetic sensor(Mag) to sense the earth's magnetic field acting as a compass, and/orweather sensors (Wx) to relay local atmospheric conditions includingwind speed and direction, all of which is packaged together with one ormore processors (P1, P2). The measured range to each (or select set of)SWIR sample(s) can be used to correct the parallax offset between thatSWIR sample and its corresponding location in the visible RGB image,using the known spacing of the SWIR, RGB, and LRF sensors.

As shown in FIG. 3, one processor (P1) is associated with themultispectral SWIR camera and is responsible for real-time or nearreal-time processing of the SWIR multispectral data to create the gasabsorption imagery. A separate processor (P2) has a path for acceptingthe visible camera (RGB) imagery and triggers the other low-bandwidthsensors (LRF, GPS, IMU, Mag). This processor (P2) also communicateswirelessly (or wired) with an external weather sensor (Wx) and agraphical user interface (GUI) implemented on a touch-screen tabletcomputer. The tablet, in turn, provides wireless access to an Ethernetor data cloud (E/C), which in turn can be accessed by a remote personalcomputer (PC). This arrangement enables remote (PC) access and controlof one or more gas imaging sensor systems. Finally, an artificialilluminator (Lum) is controlled by a micro-controller (C2) andincorporated to enable gas imaging in the absence of sufficient sunlightor for indoor locations. Design concepts for scanning SWIR illuminatorsare described in U.S. Prov. Pat. Appl. No. 62/587,304, incorporatedherein by reference. Alternative implementations are possible, such asfor example (but not limited to) a configuration with:

the display or the controls or the complete user interface physicallyattached to the imaging device;

the display or the controls or the complete user interface physicallyremote from the imaging device;

the user interface implemented with physical knobs, buttons, sliders,dials, selectors or similar on the imaging device or separate from it;

the user interface implemented with digital representations of knobs,buttons, sliders, dials or similar using a display where this displaycan be either physically attached to the imaging device or connected bywired or wireless means;

a combination of physical and digital user interface described above;

processors P1 and P2 combined into a single processor or their functionsdistributed over multiple processors;

some or all of the low-bandwidth sensors being integrated into (a) theimaging device, (b) into a separate unit, or (c) into a display unit;and

some or all of a single set of low-bandwidth sensors being connected toone or several processors that is (are) providing data for use bymultiple imaging sensor systems.

With the imaging sensor system of FIG. 3 properly calibrated, mountedatop a mast located next to a site of interest, such as illustrated inFIG. 4, the sensor can be scanned around the boundary of the site (inone embodiment, with an approximate size of 15 meters on a side) so asto create an optical sheath that envelops and covers the site and anyequipment located upon the site. If a gas leak is present, the gas willmigrate (due to buoyancy, wind, and diffusion) so as to cross some partof the optical sheath. This will result in selective absorption of theillumination within the multiple spectral bands, indicative of theparticular species and amount of gas. Thus, a rapid boundary scan isused to detect the existence of a leak. Once so detected, a change ofscan pattern is automatically triggered. The more focused scan patternwithin the optical sheath enables localization of the leak on the site.Upon localizing the leak to within a predetermined extent, the scanningsensor automatically switches to a raster scan pattern of the areaaround the leak. By spatially oversampling the sensor data whilescanning, a progressive-resolution image is constructed usingsuper-resolution processing techniques. This results in a sequence ofincreasing resolution imagery around the leak, whereby the resolution ofan image pixel exceeds the sampling resolution of the detector itself.Super-resolution image processing methods are well documented in theopen literature.

In one embodiment shown in FIG. 4, a sensor and illuminator are mountedon a pan-tilt unit atop a mast, for example adjacent to a gas wellpadand having the well pad within a respective optical sheath monitored bythe sensor. As the sensor scans a closed boundary of the site, itcreates an optical sheath that envelopes the site.

In FIG. 5A, a plan view is shown of a square site, such as the gaswellpad of FIG. 4, having dimensions of L×L. In an exemplary,non-limiting embodiment, L equals 10 meters. The sensor S is located atone corner of the site. As the sensor pans in angle across the site inazimuth, about a vertical axis of rotation, the line of sight of thesensor traces out polar arcs on the ground plane. As the sensor tilts inelevation, about a horizontal axis of rotation, the sensor line of sighttraces radial lines on the ground plane.

With respect to FIG. 5B, a side view of an L×L site is presented. Thesensor S is mounted atop a mast of height ¾L, disposed in one corner ofthe L×L site. Tilt angles relative to the horizontal are shown as raysfrom the sensor to various locations on the ground plane, including inthe opposite corner of the site, at 28° from a horizontal planeextending from the sensor. Other exemplary rays are illustrated,including one intersecting the ground plane a distance L in the groundplane from the mast and 37° from horizontal, and one intersecting theground plane a distance 0.07L in the ground plane from the mast and 85°from horizontal.

In FIG. 6, a square site, covered by a polar coordinate raster scanpattern relative to the sensor S, is presented in plan view. This rasterscan pattern provides full coverage over the site and is suitable formonitoring gas emissions across the illustrated site. Such a polar scanprovides multispectral imagery that can be super-resolved intohigh-resolution imagery for the detection and quantification ofemissions from anywhere within the site.

A square site with a boundary scan pattern performed relative to asensor S is illustrated in FIG. 7. This boundary scan can be performedrapidly to detect the presence of a gas leak somewhere within the site.Such a scan pattern is suitable for gas safety applications.

In FIG. 8, a plan view of a square site with a localization scan patternoverlaid on the boundary scan pattern of FIG. 7 is shown. This scanpattern divides the site into sectors, each with its own resultingoptical sheath. This enables localization of a leak to within one of thesectors based on the measured wind direction. Further division of thissector into sub-sectors, as shown, localizes the leak, for example, toone quadrant of a sector. A shifted sector can then be defined aboutsuch an identified quadrant. For example, in FIG. 9, illustrated is aplan view of a square site having a local polar raster scan across theshifted sector within which a leak has been localized. The angularextent of the sector image is approximately 30×20 degrees in theillustrated example, or 500×400 milliradians.

Operation of Sensor Embodiments

FIG. 10A illustrates a real-time absorption image of a methane gas jetexiting a 1 mm diameter round orifice with an internal pressure of 1300psig (pounds per square inch—psi “gauge”, i.e., relative to externalatmospheric pressure of approximately 14.5 psi). The absorption image iscolored according to a pixel-level differential optical depth scaleshown to the right. This pixel-level differential optical depth isdirectly proportional to the number of methane molecules along each coneof rays between the light source and the photo-detector corresponding toeach pixel; this is the so-called pixel column density of the gas. Theturbulent structure of the jet is apparent near the top of the jetimage. It is clear from the absorption image that the jet diameter growslinearly along the jet axis, as is consistent with the theoreticalself-similar solution for turbulent jets. In this image, it is the noiselevel of the background differential optical depth that determines theboundary of the jet and so limits the visible diameter.

FIG. 10B shows cross-sectional profiles of the jet absorption image. Thegraphs plot differential optical depth vs. pixel number across a row of512 pixels corresponding to the horizontal lines labeled a, b, c in FIG.10A. It is apparent from these plots that the diameter of theseabsorption profiles is increasing along the jet axis, and that theturbulence creates fluctuations in absorption through the jet. Thegeneral shape of these plots is entirely consistent with the path lengththrough a cross-section of a round jet in combination with a radialconcentration profile of Gaussian shape. Superposed on this smooththeoretical profile are fluctuations in concentration due to turbulence.

The maximum of the absorption on each profile should occur on axis ofthe jet, if the imaging line-of-sight is perpendicular to the jet axis,as this is where the path length through the jet is a maximum and thegas concentration is largest. Based on the self-similar solution forturbulent round jets, the gas concentration on axis will decreaselinearly along the jet as it expands, while the diameter increaseslinearly along the axis, and so the product of axial gas concentrationwith diameter should remain a constant, suggesting the column densityalong the jet axis should remain constant. However, due to the turbulentfluctuations, these profiles change over time, and so individual pixelvalues fluctuate. To cope with these turbulent fluctuations, it issuggested to use spatial averages of quantities across the jet, and thencalculate the total absorption of a slice of jet, as it is due to thetotal mass of gas in that slice and not sensitive to the exactdistribution of mass throughout the slice. Each row of pixels alongconsecutive cross-sections through the jet corresponds to a constantthickness slice, and since the jet diameter varies linearly with axialdistance, hence, the slice volume increases as the square of the axialdistance. But since the gas concentration dilutes linearly with axialdistance in a self-similar round jet, the mass of gas in constantthickness slices is expected to increase linearly with axial distancealong the jet. That is, the gas at the front of a jet slice flows slowerthan the gas at the rear of the jet slice, causing mass to build upbetween slices of constant thickness. And since the mass of gas inslices increases linearly along the jet axis, so should the absorptiondue to that mass. Thus, the integrated differential optical depth acrosseach cross-section of the jet image should increase linearly along thejet. Similarly, the jet width in the absorption image should increaselinearly along the jet, where the jet boundary is determined by thenoise in the background image. Integrating the absorption across jetcross-sections acts to smooth out the effect of turbulent fluctuationson gas concentration in the jet.

FIGS. 11A and 11B plot the automatically extracted jet width andcorresponding integrated differential optical depth (integrated-dOD),respectively, along the axial distance (approximately the image rownumber) for the jet image in FIG. 10A. It is apparent that bothquantities follow clear linear trends, and so a least-squares regressionline is fit to each quantity. Forming the ratio of integrateddifferential optical depth to jet width yields an average differentialoptical depth (Avg-dOD) value at each axial location along the jet. Thisratio is plotted in FIG. 11C, to which a least-squares regression lineis fit (starting away from the orifice to exclude the complex acousticregion just outside the hole). It is apparent from FIG. 11C that theslope of this regression line is very small, and that the intercept ofthe regression line then corresponds to the average differential opticaldepth extrapolated back to the effective orifice from which the gasleaks under pressure.

FIG. 12 plots the integrated differential optical depth (integrated-dOD)along the axis of a natural gas jet emanating from a narrow (50 micron)slit orifice that is 1 cm long, meant to emulate a crack (instead of ahole) in a pressurized line at 60 psig. Following the same reasoning asabove but for a plane turbulent jet (instead of a round turbulent jet),one finds that the integrated-dOD should scale with the square-root ofthe distance along the axis, as is apparent from the least-squaresregression fits in FIG. 12. And since the integrated-dOD across a planejet is independent of the orientation of the slit relative to theline-of-sight of the sensor, one can use this square-root versus linearbehavior to distinguish between a gas leak emanating from a crack or ahole.

Absorption and Mass Flow Across a Range of Pressures and Orifice Sizes

Experiments have been conducted to image the release of methane gasunder a range of pressures (50-1400 psig) exiting from round orifices(diameters of 0.75 mm and 1.0 mm). Gas jet boundaries are automaticallyextracted from the imagery, and the average differential optical depth(Avg-dOD) along the jet axis is computed. Fitting a least-squaresregression line to this data determines the intercept of this regressionline, which indicates the degree of absorption of the methane at theeffective orifice.

FIG. 13A plots the value of this Avg-dOD intercept (scaled by orificediameter) against the internal pressure P (in units of Bar, where 1Bar=14.5 psi, the atmospheric pressure at sea level) for round orificesof 1 mm and 0.75 mm. The data points are consistent with a power-lawbehavior of pressure, for which the scaling constant and exponent valuesare shown on the graph. This is expected since the absorption by themethane gas at the effective exit hole (extrapolating back from thelinear boundaries of the jet) will be proportional to the product of theeffective orifice diameter and the local gas density, while the gasdensity is proportional to a power-law of the pressure through theadiabatic equation of state using the ratio of heat capacities formethane. Further experiments will determine the general utility of thisspecific power-law relationship across a range of orifice diameters and(approximately round) shapes.

FIG. 13B plots the measured methane mass flow per orifice area (ingrams/sec, divided by orifice area) against internal pressure fornumerous experiments using round and slit orifices of different sizes.It is clear they follow the expected linear relationship, with a slopedetermined by the data. The mass flow out of the orifice is proportionalto the product of the area of the orifice and the methane gas density inthe pipe (which is proportional to the pressure in the pipe). Thus,while the Avg-dOD intercept curve scales linearly with effectivediameter of a round orifice (as implied by FIG. 13A), the mass flowscales like the square of the effective diameter of a round orifice (asimplied by FIG. 13B). These relationships taken together are thereforeused to estimate the orifice size and mass flow of gas directly from theobserved absorption image of a gas jet leaking from a hole under knowninternal pressure. Thus, it is possible to estimate the size of a leakhole directly from a gas jet absorption image, even if the leak holeitself is not visible in the image. And this leads directly to a leakrate or mass flow estimate. Similar relationships apply to a planar gasjet leaking from a narrow crack.

FIG. 14A shows an example gas absorption image of 100% methane exitingfrom a 0.38 mm round orifice at an exit pressures of 938 psig in wind.Experiments were conducted outdoors in natural sunlight under varyingcrosswinds. The instrumented mass flow was measured as 70 grams/minuteof methane. The mass flow estimated directly from the imagery using theinvented methods is 74 grams/minute.

FIG. 14B graphs the data obtained using the setup in FIG. 14A.Specifically, FIG. 14B compares imagery estimated methane mass flows toinstrumented measurements for a set of experiments conducted withround-hole orifices at various exit pressures, in winds measured between0-10 miles per hour. Mass flow estimates are shown to agree well withinstrumented “ground truth” measurements as taken up to 150grams/minute. Data is presented for winds of 0-3 mph, 3-6 mph, and >6mph. This validates the method for estimating gas leak rate fromabsorption imagery, for holes in pressurized lines.

An example of gas imaging is shown in FIG. 15A, where natural gas isleaking from an underground pipe in municipal gas infrastructure inBoston, Mass. Gas emissions due to a leak in the underground pipelineare detected and overlaid on the background visible image. Alldetections as illustrated were confirmed using a flame ionization gassensor to sample the air above each surface emission area. By the timethe gas percolates up through the soil, it is approximately the sametemperature as the ground itself. A sensor system such as presentlydisclosed can image the gas emissions from the surface in sunlight asshown, or alternatively using artificial illumination (possibly mixedwith sunlight) from above reflecting off the ground, which is absorbedas it passes through the gas twice. FIG. 15A illustrates the patchynature of ground surface emissions, with gas emerging from manholes,storm gratings, cracks in road asphalt and concrete sidewalks, as wellas along the side of the road where the asphalt meets dirt and grass.All of these surface emissions may be due to a single leak in a pipe atthe bottom of the hill near the end of the street. The spatialdistribution of surface leak patches can be useful in bounding theactual leak location in the underground pipe.

FIG. 15B shows an example absorption image of natural gas leaking from asmall pipe at 4 feet below the surface of a field. The pipe is fed bythe Montreal municipal gas network pressurized to 60 psig. The locationof maximum surface emission is clear from the color overlay of gasabsorption, and was confirmed using a gas sniffer.

A plan view of a surface patch emitting methane (or natural gas) at rateQ_(m) grams/sec on average within an irregular boundary is shown in FIG.16A. Ground-level winds, of speed V in the direction shown, determinethe orientation of the bounding rectangular of dimensions L_(p) alongthe wind direction and W_(p) across the wind direction. In steady winds,the emission flux up from the ground balances the flux of methaneflowing across the downwind boundary.

FIG. 16B illustrates a side view of the sensor S mounted atop a mast onthe ground within a wide-area surface emission site. The methane fluxΘ_(m) (per unit area) out of the ground establishes a stratified methaneatmosphere above the ground, wherein this emission flux balances thebuoyancy driven upward flow of methane.

Next, the mathematical formulation of absorption imaging andquantification of gas leaks is described, using methane or natural gasas a specific example.

Defining the SWIR Spectral Bands

Spectral data is collected through multiple filters, each of bandwidthapproximately 100 nm with transmission greater than 5%, spanning thewavelength region approximately 1950-2500 nanometers (i.e., 1.95-2.50microns). This data provides coverage of spectral features thatcharacterize methane, ethane, propane, butane, carbon dioxide, ammonia,and possibly other gases of interest, yet avoids the strong water vaporabsorption features, as illustrated in FIG. 1A. The data is organizedinto multiple spectral bands, for example five bands as illustrated inFIG. 1B. The data itself is collected in real-time by the SWIR sensor asit points in directions in space that correspond to locations on theground, or objects on the site, being monitored by the scanning SWIRsensor. In terms of spatial patterns, the data corresponds to one ofseveral possible scan patterns including raster-scanned imagery over aprescribed field-of-view.

One of the multiple spectral bands is selected to include only weak orno features of the gases of interest, and is referred to as the“reference band,” as exemplified by the 100 nm wide band centered near2100 nm in FIG. 1B. Refer to this spectral band filter as the ReferenceFilter with transmission F_(ref)(λ) and integrated transmission F_(ref).

The other spectral band filters are simply referred to as SpectralFilter B (where B is the band number), each with transmission F_(B)(λ),and integrated transmission F_(B).

Data collected at each spectral band will be corrected for theintegrated transmission associated with its corresponding spectralfilter F_(B), to form I_(B) the intensity in band B. The intensity ofeach band is then measured relative to I_(ref), the data collected atthe reference band corrected by the transmission of the reference filterF_(ref). The resulting transmission corrected data are a set of spectralband ratios forming a spectral pattern P_(B) (a vector) defined as:

P _(B)≡Set of Band Ratios{I _(B) /I _(ref)}  (Eq. 1)

Each gas of interest is characterized by its own spectral pattern ofband ratios, and will be detected in the measured data by spectralpattern recognition methods, including spectral pattern unmixing in thecase of gas mixtures. It can be shown that the 5-element spectralpatterns associated with the gases shown in FIG. 1B enable separation ofthe gases of interest, including mixtures that characterize natural gasfrom geographically different locations and from processed distributiongas. Separation of pure methane from distribution gas is the mostchallenging, as distribution gas is typically 95% methane. It can beshown that even they can be separated up to noise levels that are 10% onaverage. The selection of spectral bands can be tailored to speciate andnot confuse a desired set of gases and mixtures, or group togethernumerous gases (e.g., heavy hydrocarbons) to be recognized as a gaswithin the group.

Adapting the Sensor to the Ambient Environment

Denote the optical depths in each spectral band B, including thereference band, as measured in the ambient environment as τ_(B) ^((a))and τ_(ref) ^((a)).

They are the products of the absorptivity and r, the path length throughthe environment. The band intensities resulting from the radiativetransfer are:

I _(B) ^((a)) =S _(B)(r)Q _(B) F _(B) R _(B)exp−[τ_(B) ^((a))]  (Eq. 2a)

I _(ref) ^((a)) =S _(ref)(r)Q _(ref) F _(ref) R _(ref)exp−[τ_(ref)^((a))]  (Eq. 2b)

Here, S_(B) is the illumination source function (combining both solarand artificial illumination), Q_(B) is the quantum efficiency of thedetector, F_(B) is the integrated transmission of the filter, and R_(B)is the reflectance of the background material (which can be acalibration panel or the natural surrounding materials), allcorresponding to spectral band B and similarly for the reference band.

Form the pattern P_(B) of spectral band ratios, and note the spectralillumination source function ratio S_(B)/S_(ref) is independent of pathlength r and only a function of wavelength.

Define the cross-channel gain G_(B), ambient spectral differentialabsorption coefficient δα_(B) ^((a)) and path length L_(R) from sensorto a reflector panel. Then, form the ration of Eq. 2a and Eq. 2b toobtain:

$\begin{matrix}{\frac{I_{B}^{(a)}}{I_{ref}^{(a)}} = {{\frac{\left\lbrack {{S_{B}(0)}Q_{B}F_{B}R_{B}} \right\rbrack}{\left\lbrack {{S_{ref}(0)}Q_{ref}F_{ref}R_{ref}} \right\rbrack}\exp} - \left\lbrack {\tau_{B}^{(a)} - \tau_{ref}^{(a)}} \right\rbrack}} & \left( {{{Eq}.\mspace{14mu} 3}a} \right) \\{\frac{I_{B}^{(a)}}{I_{ref}^{(a)}} = {{{G_{B}\exp} - {2{L_{R}\left\lbrack {\alpha_{B}^{(a)} - \alpha_{ref}^{(a)}} \right\rbrack}}} = {{G_{B}\exp} - {2{L_{R}\left\lbrack {\delta\alpha}_{B}^{(a)} \right\rbrack}}}}} & \left( {{{Eq}.\mspace{14mu} 3}b} \right)\end{matrix}$

where

$\frac{\left\lbrack {{S_{B}(0)}Q_{B}F_{B}R_{B}} \right\rbrack}{\left\lbrack {{S_{ref}(0)}Q_{ref}F_{ref}R_{ref}} \right\rbrack}$

of Eq. 3a is corresponds to G_(B) of Eq. 3b, and [τ_(B) ^((a))−τ_(ref)^((a))] of Eq. 3a corresponds to 2L_(R)[δα_(B) ^((a))] of Eq. 3b.

The SWIR illumination bouncing off a calibration reflector panel (anexample of which is Spectralon) is measured in each spectral band B attwo distances, the spot or image average intensities are calculated, andthe log of their ratio is formed to solve for the unknowns G_(B) andδα_(B) ^((a)) (or use more than two distances and solve for the unknownsvia least squares).

Each gain G_(B), as defined in Eq. 3B, incorporates the ratios of filterband transmissions, detector quantum efficiencies, and bandreflectivities of the calibration panel. Each gain G_(B) is rescaled(utilizing in-scene background reflectors) by the ratio of in-scene bandreflectivities. δα_(B) ^((a)) and spectral samples of the in-scenebackground materials (cement, asphalt, dirt, grass, etc.) are used todetermine the rescaled gain G_(B) for each reflecting material. It isdesired, but not essential, that the sensor automatically recognize thebackground materials that comprise a site being inspected or monitored.

Detecting and Imaging Gas Leaks

The sensor system samples or images in the direction of a possible gasleak of extent D_(J) (e.g., jet width) and measures/senses the rangeL_(R) to the reflecting surface in the background (either the reflectorpanel or in-scene material serving as a reflector).

Let τ_(B) ^((g+a)) be the band-B optical depth of the combined possiblegas jet in the ambient environment from the sensor to the reflector atL_(R) and back to the sensor. Then the intensities in the bands(including reference band) are:

I _(B) ^((g)) =S _(B)(r)Q _(B) F _(B) R _(B)exp−[τ_(B) ^((g+a))]  (Eq.4a)

I _(ref) ^((g)) =S _(ref)(r)Q _(ref) F _(ref) R _(ref)exp−[τ_(ref)^((g+a))]  (Eq. 4b)

Form each ratio of spectral band intensities, substitute the expressionfor the cross-channel gain (rescaled for background surface reflector),define the differential spectral absorption coefficient of gas δα_(B)^((a)) and rearrange terms:

$\begin{matrix}{\frac{I_{B}^{(g)}}{I_{ref}^{(g)}} = {{G_{B}\exp} - \left\{ {{2{D_{J}\left\lbrack {{\delta\alpha}_{B}^{(g)} - {\delta\alpha}_{ref}^{(a)}} \right\rbrack}} + {2{L_{R}\left\lbrack {\delta\alpha}_{B}^{(a)} \right\rbrack}}} \right\}}} & \left( {{Eq}.\mspace{14mu} 5} \right)\end{matrix}$

Define the Excess Differential Spectral Absorptivity of the gas leak(for example, diluted natural gas) over that of the ambient atmosphereenvironment:

Δ_(B) ^((g−a))≡δα_(B) ^((g))−δα_(B) ^((a))=[α_(B) ^((g))−α_(ref)^((g))]−[α_(B) ^((a))−α_(ref) ^((a))]  (Eq. 6)

So the Differential Spectral Optical Depth image due to the gas leak isobtained from the measured spectral intensities and calibrationparameters:

$\begin{matrix}{{\delta \; {OD}_{B}} = {{\left\lbrack \Delta_{B}^{({g - a})} \right\rbrack D_{J}} = {{{- \frac{1}{2}}{\ln\left\lbrack {\frac{1}{G_{B}}\frac{I_{B}^{(g)}}{I_{ref}^{(g)}}} \right\rbrack}} - {\left\lbrack {\delta\alpha}_{B}^{(a)} \right\rbrack L_{R}}}}} & \left( {{{Eq}.\mspace{14mu} 7}a} \right)\end{matrix}$

In the case of negligible atmospheric absorption over range 2r comparedto the gas leak itself, the 2^(nd) term on the right can be neglected,yielding:

$\begin{matrix}{{\delta \; {OD}_{B}} = {{- \frac{1}{2}}{\ln\left\lbrack {\frac{1}{G_{B}}\frac{I_{B}^{(g)}}{I_{ref}^{(g)}}} \right\rbrack}}} & \left( {{{Eq}.\mspace{14mu} 7}b} \right)\end{matrix}$

The factor of ½ comes from the double path length through the gas due toreflection of incident light off the background at range r. In the caseof single pass transmission (e.g., sunlight through the gas), thisfactor is dropped.

Estimating Jet Mass, Orifice Size and Methane Mass Flux for PressurizedLeaks

Use the differential spectral optical depth image for a detected jet (orplume or cloud), compute the average δOD_(B) across the profiles alongits axis z, and sum along the axis to obtain total spectral opticaldepth of the gas.

δOD_(B) ^((jet))=Σ_(axis) D _(J)(z)δOD _(B)(z)  (Eq. 8)

Relate δOD_(B) to column density to obtain total number of methanemolecules (or other detected species), multiply by the mass of a methane(or other detected species) molecule to obtain total mass of gas in thejet (or plume or cloud).

$\begin{matrix}{{Mass}_{{CH}_{4}} = {\left\lbrack \frac{\delta \; {OD}_{B}^{({jet})}}{\sigma_{B} - \sigma_{ref}} \right\rbrack m_{{CH}_{4}}}} & \left( {{Eq}.\mspace{14mu} 9} \right)\end{matrix}$

Using the differential spectral optical depth sensed along the axis of adetected jet, derive the average-δOD_(B) linear fit intercept, andcombine this with the following power law equation that was discussedpreviously (see FIGS. 11C, 12, and 13A),

$\begin{matrix}{{\delta \; {OD}_{B\; 0}} = {\frac{1}{S}D_{O}P^{k}}} & \left( {{{Eq}.\mspace{14mu} 10}a} \right)\end{matrix}$

Solve Eq. 10a for (round) hole diameter D_(O) and use the scale factorand exponent from the experimental data in FIG. 13A (rescaling pressurefrom psig to Bar) and find,

$\begin{matrix}{D_{O} = {{S\; \frac{\overset{\_}{\delta \; {OD}_{B\; 0}}}{P^{k}}} \cong {110\frac{\overset{\_}{d\; {OD}_{0}}}{\left( {P/14.7} \right)^{0.34}}}}} & \left( {{{Eq}.\mspace{14mu} 10}b} \right)\end{matrix}$

This result enables us to estimate the mass flow rate (g/min) from thehole by utilizing the orifice flow data fit equation of FIG. 13B,

$\begin{matrix}{Q_{m} = {{\frac{\pi}{4}{D_{O}^{2}\left( {0.68P} \right)}} \cong {0.53D_{O}^{2}P}}} & \left( {{Eq}.\mspace{14mu} 11} \right)\end{matrix}$

This mass flow estimate is valid for P above 1.8 Bar (˜27 psi), so theflow is chocked (i.e., critical) at the orifice, with outflow at thelocal sound speed.

The units of the above quantities are:

-   -   Differential spectral optical depth δOD_(B) is dimensionless    -   Round-hole diameter D_(O) in mm    -   Pressure (interior or inline) P in psig    -   Mass flux Q_(m) in grams/min        Relationships equivalent to Eqs. 10b and 11 can be written among        these quantities if expressed in units other than those used        here. There are many systems of physical units that are        customary in different countries and regions of the world.        Methane Mass Flux from Surface Patch Emissions Under Steady        Winds

As shown in FIG. 16A, a surface patch by definition is isolated,surrounded by ambient clear air, with winds that are steady in directionand speed V. Gas emerges from the ground (or a tank), diffuses into theair above, and rises (methane) or falls/lingers (for heavierhydrocarbons) due to buoyancy forces. The wind convects the gas downwindas it continues to disperse and rise.

The mass of methane associated with a surface patch is estimated fromspectral imaging, which provides the differential spectral optical depthof methane over the entire patch. Therefore, one can sum the pixels overthe entire patch, similar to Eq. 8 for a gas jet, and convert the resultto total methane mass over the patch, analogous to Eq. 9.

Measure the wind direction and speed V near ground/surface level, andassume it is representative of the wind at the emitting surface patch.Also measure range from the sensor to the surface patch, so that pixelangular dimensions in the image of the patch can be converted to lineardimensions.

The vertical flux of methane due to buoyancy is generally negligiblecompared to the horizontal mass flux due to a mild wind as it crossesthe patch. The steady wind V (cm/sec) blows methane across the patch andaway, as it diffuses out of the ground into the air above the patch.Thus, an equilibrium is established in which the surface emission massflux Q_(m) is balanced by the windblown mass crossing the downwindboundary of the patch. This enables us to estimate the surface emissionmass flux of methane.

The shallow methane diffusion layer above the surface patch has acharacteristic thickness D and concentration c, which give rise to themeasured differential optical depth δOD_(B) at each pixel. Select athreshold for the optical depth at a desired level to delineate theboundaries of the patch. Construct the bounding rectangle around thatpatch, such that one axis of the rectangle aligns with the winddirection, as illustrated in FIG. 16A. Using the range measured to thepatch, convert the pixel dimensions of this bounding rectangle to lineardimensions L×W (cm). The volume flux (cm³/sec) across the downwindboundary of the patch is equivalent to the volume flux DWV across side Wof the bounding rectangle. The methane mass flux Q_(m) (grams/sec) isobtained from the product of methane concentration in the diffusionlayer, methane mass density at standard temperature and pressure (STP),and volume flux across the downwind boundary;

Q _(m) =cρ _(CH) ₄ DWV  (Eq. 12a)

Expressing cρ_(CH) ₄ D in terms of the differential spectral opticaldepth δOD_(B), obtain the estimate of methane mass flux from a surfacepatch in a steady wind:

$\begin{matrix}{Q_{m} = {\left\lbrack \frac{m}{\sigma_{B} - \sigma_{ref}} \right\rbrack_{{CH}_{4}}{{WV}\left( {\delta \; {OD}_{B}} \right)}}} & \left( {{{Eq}.\mspace{14mu} 12}b} \right)\end{matrix}$

Methane Mass Flux from Surface Patch Emissions Under Gusting Winds

As shown in FIG. 16A, this assumes each surface patch (or tank vent) isisolated, surrounded by ambient clear atmosphere, with winds that aregusting, whereas the result in Eq. 12b assumes winds that are steady.The methane emerges from the ground (or vent), diffuses into the airabove, and rises due to buoyancy forces. Heavier hydrocarbons will fall(or linger) due to negative (or neutral) buoyancy. However, when a gustoccurs, the wind rapidly blows the entire layer of methane (or heaviergas) away from the surface patch.

In gusting winds, the methane layer above the patch alternates betweenbuilding itself up by diffusion out of the surface (in steady winds ofspeed V) and being rapidly destroyed by a sudden gust of wind. Thisallows the build-up of a methane layer to be observed over time. Thus,the increase of methane mass above the patch is due to the surfaceemission mass flux Q_(m), minus the mass flux due to transport by asteady wind V as in Eq.12B:

$\begin{matrix}{\frac{{dM}_{{CH}_{4}}}{dt} = {Q_{m} - {\left\lbrack \frac{m}{\sigma_{B} - \sigma_{ref}} \right\rbrack_{{CH}_{4}}{{WV}\left( {\delta \; {OD}_{B}} \right)}}}} & \left( {{{Eq}.\mspace{14mu} 13}a} \right)\end{matrix}$

Direct observation of the accumulation of methane is possible by imagingthe time-varying differential optical depth over the patch, since

$\begin{matrix}{\left\lbrack \frac{{dM}_{{CH}_{4}}}{dt} \right\rbrack_{obs} = {{A_{p}\rho_{{CH}_{4}}\frac{d}{dt}({cD})} = {\left\lbrack \frac{m}{\sigma_{B} - \sigma_{ref}} \right\rbrack_{{CH}_{4}}A_{p}\frac{d}{dt}\left( {\delta \; {OD}_{B}} \right)}}} & \left( {{{Eq}.\mspace{14mu} 13}b} \right)\end{matrix}$

A_(p) is the area of the patch (or vent) observed before the gust, D isthe changing thickness of the methane layer above the patch, and c isthe increasing concentration of methane as the diffusion layer growsuntil the next gust.

Equating expressions Eq.13a and Eq.13b, we obtain an estimate of themethane mass flux Q_(m) (grams/time) from a surface patch (or vent) ingusting wind, by observing the time-varying differential optical depthas the methane layer is reestablished under steady wind conditions;

$\begin{matrix}{Q_{m} = {\left\lbrack \frac{m}{\sigma_{B} - \sigma_{ref}} \right\rbrack_{{CH}_{4}}\left\{ {{A_{p}\frac{d}{dt}\left( {\delta \; {OD}_{B}} \right)} + {{WV}\left( {\delta \; {OD}_{B}} \right)}} \right\}}} & \left( {{{Eq}.\mspace{14mu} 13}c} \right)\end{matrix}$

Methane Mass Flux from Wide-Area Surface Emissions

FIG. 16B illustrates the geometry of monitoring wide-area surfaceemissions, where the sensor itself is located within or next to the areaof interest. Examples include landfills, open face mine pits on land,and tailing ponds where the surface is water. Methane is released frommaterials at or below the surface, and is emitted across a wide surfacearea. In this scenario, the effects of buoyancy in vertical masstransport outweigh the effects of horizontal winds.

Although surface emissions can be non-uniform, horizontal winds onlyserve to mix the diluted methane layer so as to become horizontally moreuniform as it rises above the surface. The horizontal winds do notgenerate a net source or sink of methane (except at the distant downwindboundary of the area). The methane forms a vertically stratifiedatmosphere, diluted by air, rising due to positive buoyancy and possiblevertical convective air currents. Strong convective currents reduce thevertical stratification, leading to a nearly uniform concentration overthe wide-area surface.

An equilibrium is established in which the surface emission flux (gramsper time per unit area) sustains the vertical methane-in-air atmosphere.The sensor can measure the differential spectral optical depth betweenthe sensor and the surface (for SWIR reflective surfaces like ground,but not water). Alternatively, the sensor can measure the optical depthbetween the sensor and a boundary of the emitting area by sensinghorizontally or upwards, with the sun (or an illuminator) transmittingthrough the methane atmosphere. An illuminator can also be located on aplatform at a distance from the sensor, with the light transmittingthrough the methane atmosphere towards the sensor. For example, thistype of probing of an extensive methane-in-air atmosphere can beaccomplished by tracking the sun over an open-pit mine or over atailings pond, in order to estimate the vertical methane flux, as is ofinterest in the Canadian oil sands.

Consider the sensing geometry as shown in FIG. 16B, with the sensor atheight H above the emitting surface, tilted downwards at an angle ϕ. Thepath length through the stratified methane layer to the surface isH/cos(ϕ). The sensor can be mounted on a mast or on an overlook, orflying above the surface sensing downwards. It is required that theoptical path through the methane atmosphere not be optically thick(optical depth less than approximately 3) so that the sensor receivessufficient signal from light reflected off the surface.

The vertical flux of methane mass per unit surface area Θ_(m) isconstant with height above the surface z, as methane mass is conservedas it rises in steady state:

Θ_(m)=Θ_(m)(0)=Θ_(m)(Z)=ρ_(CH) ₄ c(z)v _(z)(z)  (Eq. 14)

where c(z) is the methane concentration profile and v_(z)(z) is thevertical velocity profile of the rising methane.

The vertical velocity profile is due primarily to the buoyancy force asthe methane gas displaces the heavier air around it, where the air is inhydrostatic equilibrium and exerts downward pressure on the surface. Asmethane gas rises, it gains speed under gravity g according to,

$\begin{matrix}{\frac{\partial v_{z}}{\partial t} = {{{- \left\lbrack \frac{\rho_{{CH}_{4}} - \rho_{a}}{\rho_{a}} \right\rbrack}g} = {{- \left\lbrack \frac{\Delta\rho}{\rho_{a}} \right\rbrack}g}}} & \left( {{{Eq}.\mspace{14mu} 15}a} \right)\end{matrix}$

where Δp is the reduced density of methane relative to its ambientsurroundings. If we neglect second-order effects associated with methanerising through an atmosphere of already reduced density (due to thepresence of low concentration methane mixed with air), we can treat theambient density as approximately the density of clear air itself nearthe surface, and treat it as constant. Integrate Eq.15a over time toobtain the velocity and position following a gaseous element as itrises, and solve for the vertical velocity field as a function ofheight, as the methane atmosphere is assumed to be in a steady state.

$\begin{matrix}{{v_{z}(t)} = {{- \left\lbrack \frac{\Delta\rho}{\rho_{a}} \right\rbrack}{gt}}} & \left( {{{Eq}.\mspace{14mu} 15}b} \right) \\{{z(t)} = {{{- 1}/{2\left\lbrack \frac{\Delta\rho}{\rho_{a}} \right\rbrack}}{gt}^{2}}} & \left( {{{Eq}.\mspace{14mu} 15}c} \right) \\{{v_{z}(z)} = {\sqrt{{2\left\lbrack \frac{- {\Delta\rho}}{\rho_{a}} \right\rbrack}g}\left( z^{1/2} \right)}} & \left( {{{Eq}.\mspace{14mu} 15}d} \right)\end{matrix}$

Substitute Eq.15d into Eq.14 to obtain the vertical mass flux per area:

$\begin{matrix}{\Theta_{m} = {\rho_{{CH}_{4}}{\sqrt{{2\left\lbrack \frac{- {\Delta\rho}}{\rho_{a}} \right\rbrack}g}\left\lbrack {z^{1/2}{c(z)}} \right\rbrack}}} & \left( {{{Eq}.\mspace{14mu} 16}a} \right)\end{matrix}$

Since the vertical mass flux must be constant with height, Eq.16aimplies that the methane concentration profile above the surface mustvary inversely with height according to

$\begin{matrix}{{c(z)} = {c_{o}\left\lbrack \frac{z_{o}}{z} \right\rbrack}^{1/2}} & \left( {{{Eq}.\mspace{14mu} 16}b} \right)\end{matrix}$

where c_(o) and z_(o) correspond to the concentration at height z_(o)just above the surface diffusion layer where buoyancy dominates overdiffusion. The methane profile of Eq.16b is induced by buoyancy alone,it is not applicable inside the shallow diffusion layer where height ztends towards zero (i.e., there is no singularity as z approaches 0).

Substitute Eq.16b into Eq.16a to obtain the vertical mass flux per unitarea,

$\begin{matrix}{\Theta_{m} = {\rho_{{CH}_{4}}{\sqrt{{2\left\lbrack \frac{- {\Delta\rho}}{\rho_{a}} \right\rbrack}g}\left\lbrack {z_{o}^{1/2}c_{o}} \right\rbrack}}} & \left( {{{Eq}.\mspace{14mu} 16}c} \right)\end{matrix}$

Relate the differential spectral optical depth δOD_(B) to the integralof concentration profile along the optical path from sensor to surface.The sensor can be calibrated to the prevailing sunlight reflecting offthe surface, or it can utilize a SWIR illuminator mounted near thesensor (and double the optical path length). Accounting for the slantrange through the methane atmosphere due to sensor tilt-angle θ, andnoting that z_(o)<<H, we obtain

$\begin{matrix}{{\delta \; {{OD}_{B}(\varphi)}} = {{\frac{1}{\cos (\varphi)}\left\lbrack \frac{\sigma_{B} - \sigma_{ref}}{m} \right\rbrack}_{{CH}_{4}}\rho_{{CH}_{4}}{\int_{0}^{H}{{c(z)}{dz}}}}} & {{~~~~~~~~~}\left( {{{Eq}.\mspace{14mu} 17}a} \right)} \\{= {{\frac{2}{\cos (\varphi)}\left\lbrack \frac{\sigma_{B} - \sigma_{ref}}{m} \right\rbrack}_{{CH}_{4}}{\rho_{{CH}_{4}}\left\lbrack {z_{o}^{1/2}c_{o}} \right\rbrack}\sqrt{H}}} & {\left( {{{Eq}.\mspace{14mu} 17}b} \right)}\end{matrix}$

Eq.17b suggests the ϕ-dependence of optical depth is 1/cos(ϕ), so can beaveraged across tilt-angle measurements and inverted to obtain,

$\begin{matrix}{\left\lbrack {z_{0}^{1/2}c_{o}} \right\rbrack = {{1/{2\left\lbrack \frac{m}{\sigma_{B} - \sigma_{ref}} \right\rbrack}_{{CH}_{4}}}\frac{1}{\rho_{{CH}_{4}}}\frac{1}{\sqrt{H}}{\langle{{\cos (\varphi)}\left\lbrack {\delta \; {{OD}_{B}(\varphi)}} \right\rbrack}\rangle}}} & \left( {{{Eq}.\mspace{14mu} 17}c} \right)\end{matrix}$

where the angle brackets imply averaging across tilt-varying sensordata, to provide an estimate of the differential spectral optical depthstraight below (ϕ=0) the sensor at height H, denoted as δOD_(B) ^(⬇).

Combining Eq.17c with Eq.16c yields the formula to estimate verticalmethane mass flux per unit area for wide-area surface emissions fromsensor data. Adopting the following units for quantities Θ_(m)(grams/sec/cm²), σ (cm²), H (meters), and m_(CH∝)(grams), obtain thevertical mass flux per unit area due to wide-area surface emissions:

$\begin{matrix}{\Theta_{m} = {{\sqrt{{\frac{1}{2}\left\lbrack \frac{\rho_{a} - \rho_{{CH}_{4}}}{\rho_{a}} \right\rbrack}g}\left\lbrack \frac{m}{\sigma_{B} - \sigma_{ref}} \right\rbrack}_{{CH}_{4}}\frac{\delta \; {OD}_{B}^{\Downarrow}}{\sqrt{H}}}} & {{~~~~~~~~~~~~~~~~~}\left( {{{Eq}.\mspace{14mu} 18}a} \right)} \\{= {{1.5\left\lbrack \frac{m}{\sigma_{B} - \sigma_{ref}} \right\rbrack}_{{CH}_{4}}\frac{\delta \; {OD}_{B}^{\Downarrow}}{\sqrt{H}}}} & {\left( {{{Eq}.\mspace{14mu} 18}b} \right)}\end{matrix}$

CONCLUSION, RAMIFICATIONS AND SCOPE

The embodiments as described above consist of both multispectral SWIRsensors and methods for rapidly detecting, localizing and imagingmethane and other hydrocarbon gases, and methods to estimate the leakrate or mass flux. Multiple embodiments of sensor systems have beendescribed to enable imaging of gas leaks, and multiple methods have beendisclosed for estimating methane mass flux from holes in pressurizedlines, from surface patch emissions due to underground gas pipe leaks,and from wide-area surface emissions. Example imagery and leak rateestimates across a wide variety of conditions illustrate the viabilityof the sensors and methods.

Summarizing the advantages of the invention over existing alternativegas imaging technologies, we note the ability to image and quantify gasleaks using natural sunlight without the need for any thermal contrastbetween the gas and the background, the ability to image and quantifymethane in the presence of water vapor and fog, and the ability toquantify leak rates and surface emission flux in order to assess leakseverity, prioritize repairs, and monitor emissions over extendedperiods of time. These capabilities have application in gas safety, gasleak inspection, and greenhouse gas emissions monitoring.

While the above description contains much specificity, these should notbe construed as limitations on the scope, but rather as exemplificationof several embodiments thereof. Many other variations are possible. Forexample, by selecting the appropriate spectral filters in the SWIR, theinvention can be used for detecting and quantifying other gases,liquids, emulsions, powders, and solids, in addition to the ones citedabove and discussed in detail. Thus, multiple spectral filters can beselected to detect ammonia gas, which is both combustible and toxic.Also fertilizers can be detected and quantified, as can soil wetness andgeneral plant health, thus other embodiments may be well suited foragricultural assessments. Yet other embodiments can be constructed thatare well suited for detection of ammonium nitrate and its variants asused in the making of homemade explosives. Additionally, the methodsdeveloped for leak rate quantification of gases can be utilized fordetecting gases and other substances in other spectral bands, inaddition to the SWIR band. Accordingly, the scope should be determinednot by the embodiments illustrated, but by the appended claims and legalequivalents.

The foregoing description has been directed to particular embodiments.However, other variations and modifications may be made to the describedembodiments, with the attainment of some or all of their advantages. Itwill be further appreciated by those of ordinary skill in the art thatmodifications to the above-described systems and methods may be madewithout departing from the concepts disclosed herein. Accordingly, theinvention should not be viewed as limited by the disclosed embodiments.Furthermore, various features of the described embodiments may be usedwithout the corresponding use of other features. Thus, this descriptionshould be read as merely illustrative of various principles, and not inlimitation of the invention.

Many changes in the details, materials, and arrangement of parts andsteps, herein described and illustrated, can be made by those skilled inthe art in light of teachings contained hereinabove. Accordingly, itwill be understood that the following claims are not to be limited tothe embodiments disclosed herein and can include practices other thanthose specifically described, and are to be interpreted as broadly asallowed under the law.

What is claimed is:
 1. An imaging device for detecting hydrocarboncompounds within an environment, comprising: a. plural discretephoto-detectors, each responsive to light in a wavelength range of 1.0to 2.6 microns, each photo-detector having a respective electronicread-out circuit for providing respective outputs; b. plural spectralfilters, each spectral filter substantially covering a respectivephoto-detector and being transmissive to light of wavelengths spanned bya spectral feature of a hydrocarbon compound of interest; c. an opticalelement for gathering incident illumination at least in a wavelengthrange spanned by the plural spectral filters and focusing theillumination through the plural spectral filters and onto the respectivediscrete photo-detectors; d. a scanning actuator for causing theincident illumination to be received from a two-dimensional scanpattern; e. at least one signal integration and conversion circuit incommunication with the read-out circuits for selectively integrating,amplifying, and digitizing the read-out circuit outputs; f. at least onescan control circuit in communication with the scanning actuator forcontrolling the scanning actuator and the at least one signalintegration and conversion circuit for generating, by the at least onesignal integration and conversion circuit, a sequence of two-dimensionaldata elements from the integrated, amplified, and digitized readoutcircuit outputs for each of multiple spectral bands in coordination withthe scan pattern; and g. a processor in communication with the at leastone signal integration and control circuit for i. receiving thegenerated data elements and a value representative of a distance betweenthe photo-detectors and a reflective calibration target for calibratingthe data elements of each spectral band relative to data elementsassociated with a spectral feature of a hydrocarbon compound ofinterest, by determining calibration parameters comprising
 1. a darklevel offset for each spectral band,
 2. a relative gain for dataelements of interest between spectral bands, and
 3. a relativeabsorption coefficient for each spectral band characterizing the localatmosphere across multiple spectral bands, and ii. adapting the relativegain across spectral bands, using the generated data elements and thecalibration parameters, to spectral reflectivities of backgroundmaterials within the environment, and determine a differential opticaldepth among spectral bands for assessing absorption by hydrocarbonsalong said scan pattern.
 2. The imaging device of claim 1, wherein thespectral feature of a hydrocarbon compound of interest is at least tennanometers.
 3. The imaging device of claim 1, wherein the transmissivewavelengths of each spectral filter do not overlap with the transmissivewavelengths of other filters.
 4. The imaging device of claim 1, whereinthe scanning actuator is selected from the group consisting of resonantoscillating mirrors, galvanometric driven mirrors, rotatingmulti-faceted mirrors, electrically actuated micro-mirror arrays,electrically controlled rotation stages, and a dual-axis pan-tilt unit,to scan at least in two perpendicular directions.
 5. The imaging deviceof claim 1, wherein the at least one scan control circuit is incommunication with the scanning actuator to cause the incidentillumination to be received from a two-dimensional scan pattern selectedfrom the group consisting of open scan paths, closed scan paths, andraster scan paths.
 6. The imaging device of claim 1, further comprisinga frame for physically maintaining the plural spectral filters withrespect to the plural discrete photo-detectors, whereby light passesthrough each spectral filter before impinging upon the respectivediscrete photo-detector.
 7. The imaging device of claim 1, wherein theoptical element comprises at least one of a lens, a curved mirror, and adiffractive surface.
 8. The imaging device of claim 1, wherein theprocessor is further for quantifying the calibrated data elements toderive at least one of total volume, total mass, and mass flux of aquantity of hydrocarbon compounds within the environment.
 9. A methodfor detecting, localizing, and imaging one or more hydrocarbon gasemissions within a physical area to be monitored with an imaging devicehaving a multispectral sensor responsive to light in the 1.0 to 2.6micron wavelength range, a two-dimensional scanning actuator, and a dataprocessor, comprising: a. tracing, using the scanning actuator, closedoptical scan paths of the line-of-sight of the imaging device around aboundary of the area for forming an enveloping optical sheath about thearea and with a vertex at the multispectral sensor through which gasemissions may cross resulting from at least one of diffusion, pressure,buoyancy, and ambient wind conditions and for detecting the presence ofgas emissions within the area upon detecting, by the multispectralsensor, spectral absorption characteristic of one or more hydrocarbongases along the closed optical scan paths; b. tracing, using thescanning actuator, open optical scan paths, including lines and arcs, ofthe line-of-sight of the imaging device, across locations within thearea boundary for localizing gas emissions within the area boundary inresponse to detecting spectral absorption characteristic of one or morehydrocarbon gases along the closed optical path, whereby tracing openoptical scan paths subdivides the enveloping optical sheath forlocalizing the gas emissions within one or more subdivisions of thearea; and c. tracing, using the scanning actuator, raster optical scanpaths of the line-of-sight of the imaging device across at least onesubdivision of the area, in response to localizing gas emissions withinone or more subdivisions of the area, for generating two-dimensionalmultispectral imagery of the gas emissions with the multispectralsensor.
 10. The method of claim 9, wherein the multispectral sensorresponsive to light in the 1.0 to 2.6 micron wavelength range comprisesan array of plural photo-detectors, each photo-detector having arespective filter, each filter being transmissive to light ofwavelengths spanned by a spectral feature of a hydrocarbon compound ofinterest.
 11. The method of claim 10, wherein the spectral feature of ahydrocarbon feature of interest is at least ten nanometers.
 12. Themethod of claim 10, wherein the transmissive wavelengths of each filterdo not overlap the transmissive wavelengths of the other filters. 13.The method of claim 10, wherein each filter is transmissive to light ina respective band of substantially 100 nm within a 1.8 to 2.6 micronwavelength range.
 14. The method of claim 10, wherein the array ofplural photo-detectors comprises five photo-detectors.
 15. The method ofclaim 9, where the steps of tracing closed optical scan paths, openoptical scan paths, and raster optical scan paths are performed by thetwo-dimensional scanning actuator scanning the multispectral sensor withrespect to the physical area, the two-dimensional scanning actuatorcomprised of at least one of resonant oscillating mirrors, galvanometricdriven mirrors, rotating multi-faceted mirrors, electrically actuatedmicro-mirror arrays, electrically controlled rotation stages, and adual-axis pan-tilt unit, the two dimensional scanning actuator forscanning at least in two perpendicular directions.
 16. The method ofclaim 9, wherein the step of tracing raster optical scan paths forgenerating two-dimensional multispectral imagery further comprisesquantifying the gas emissions within the area covered by the rasteroptical scan path.
 17. The method of claim 16, wherein the step oftracing raster optical scan paths for quantifying the gas emissionswithin the area covered by the raster optical scan path furthercomprises deriving at least one of total volume, total mass, and massflux of the gas emissions within the area covered by the raster opticalscan path.
 18. The method of claim 16, wherein the step of tracingraster optical scan paths for quantifying the gas emissions within thearea covered by the raster optical scan path further comprisescharacterizing at least one of a size and shape of an aperture fromwhich the gas emissions originate.
 19. A method for characterizing themass flow of hydrocarbon gas emissions of a hydrocarbon of interest froman aperture or surface, the hydrocarbon of interest being at asubstantially known or measured pressure prior to being emitted from theaperture or surface, the method comprising: a. obtaining a differentialabsorption image of the gas emissions using a multispectral imagingsensor comprising i. plural discrete photo-detectors, each responsive tolight in a wavelength range of 1.0 to 2.6 microns, each photo-detectorhaving a respective electronic read-out circuit for providing respectiveoutputs, ii. plural spectral filters, each spectral filter substantiallycovering a respective photo-detector and being transmissive to light ofwavelengths spanned by a spectral feature of a hydrocarbon compound ofinterest, iii. an optical element for gathering and focusing incidentillumination at least in a wavelength range of 1.0 to 2.6 micronsthrough each of the plural spectral filters and onto the respectivediscrete photo-detector for the discrete photo-detectors to sense thefiltered incident illumination; iv. a scanning actuator for causing theincident illumination to be received in a two-dimensional scan pattern,v. at least one signal integration and conversion circuit incommunication with the read-out circuits for selectively integrating,amplifying, and digitizing the read-out circuit outputs, vi. at leastone scan control circuit in communication with the scanning actuator forcontrolling the scanning actuator and the at least one signalintegration and conversion circuit for generating, by the at least onesignal integration and conversion circuit, a sequence of two-dimensionaldata elements from the integrated, amplified, and digitized read-outcircuit outputs for each of multiple spectral bands in coordination withthe scan pattern, and vii. a processor in communication with the atleast one signal integration and conversion circuit for
 1. receiving thegenerated data elements and a value representative of a distance betweenthe photo-detectors and a reflective calibration target for calibratingthe data elements of each spectral band relative to data elementsassociated with a spectral feature of a hydrocarbon compound ofinterest, by determining calibration parameters comprising a. a darklevel offset for each spectral band, b. a relative gain for dataelements of interest between spectral bands, and c. a relativeabsorption coefficient for each spectral band characterizing the localatmosphere across multiple spectral bands, and
 2. adapting the relativegain across spectral bands, using the generated data elements and thecalibration parameters, to spectral reflectivities of backgroundmaterials within the environment, and determine a differential opticaldepth among spectral bands for assessing absorption by hydrocarbonsalong said scan pattern; and b. estimating the diameter of asubstantially round aperture from which the hydrocarbon gas is emittedor the mass flow rate of said hydrocarbon of interest based upon atleast one of i. a relationship between the average differential opticaldepth along the gas jet extrapolated to the vertex of the gas jet andthe substantially known or measured pressure with respect to thediameter or area of the aperture from which the hydrocarbon gas isemitted, ii. a relationship between the substantially known or measuredpressure and the mass flow of the hydrocarbon gas emitted from theaperture of inferred diameter or area, iii. a relationship between thedifferential optical depth and the mass flow per unit area of thehydrocarbon gas emitted from a surface of limited extent under asubstantially steady wind of measured speed and direction, iv. arelationship between the differential optical depth, a rate of change ofthe differential optical depth, and a mass flow per unit area of thehydrocarbon gas emitted from the surface of limited extent under asubstantially steady wind of measured speed and direction following awind gust, and v. a relationship between the differential optical depthderived at a height H above a wide-area surface from which thehydrocarbon gas is emitted and the mass flow per unit area of thehydrocarbon gas emitted from the surface under the influence ofbuoyancy.
 20. The method of claim 19, wherein the spectral feature ofthe hydrocarbon of interest is at least ten nanometers.
 21. The methodof claim 19, wherein the transmissive wavelengths of each spectralfilter do not overlap with the transmissive wavelengths of otherfilters.
 22. The method of claim 19, wherein the scanning actuator isselected from the group consisting of resonant oscillating mirrors,galvanometric driven mirrors, rotating multi-faceted mirrors,electrically actuated micro-mirror arrays, electrically controlledrotation stages, and a dual-axis pan-tilt unit, to scan at least in twoperpendicular directions.
 23. The method of claim 19, wherein the atleast one scan control circuit is in communication with the scanningactuator to cause the incident illumination to be received from atwo-dimensional scan pattern selected from the group consisting of openscan paths, closed scan paths, and raster scan paths.
 24. The method ofclaim 19, further comprising a frame for physically maintaining theplural spectral filters with respect to the plural discretephoto-detectors, whereby light passes through each spectral filterbefore impinging upon the respective discrete photo-detector.
 25. Themethod of claim 19, wherein the optical element comprises at least oneof a lens, a curved mirror, and a diffractive surface.